A method for constructing a base service data table structure and componentization

Abstract

The application discloses a kind of base layer service data table structure and component construction method, it is related to data management technical field, comprising the following steps: S1, establish unified time baseline, and combine phase comb index and cross-source clock offset field, construct time sequence anchor point group;S2, under the constraint of time sequence anchor point group, generate breakpoint suspect graph, and construct breakpoint confidence matrix by mutual information gradient and arrival time residual;S3, based on the constraint of breakpoint confidence matrix, obtain causal playback chain, replay time sequence fragment, calculate gap width and drift direction.The application takes unified time baseline as core, constructs time sequence anchor point to identify breakpoint, combines causal playback to obtain repair prior, uses multi-source alignment and interpolation reconstruction continuous sequence, constructs guardrail using filtering and consistency test, finally realizes breakpoint repair and closed loop correction through time reversal and supplement, improves data integrity and time sequence stability.

Description

Technical Field

[0001] This invention relates to the field of data management technology, specifically to a method for structuring and componentizing basic business data tables. Background Technology

[0002] The structuring and componentization of basic business data tables refers to the systematic analysis, classification, and structured organization of the most basic and original data tables (i.e., source tables) in various business systems. "Sorting" involves identifying, classifying, and optimizing the fields, data types, business logic relationships, and inter-table dependencies of these source tables. "Component building" involves abstracting the sorted data tables and their relationships into reusable, standardized data components with clear input / output interfaces and unified specifications. This method transforms complex and fragmented basic business data resources into hierarchical and modular foundational data components, providing efficient and reliable support for subsequent data governance, business modeling, process optimization, and information system integration, thereby achieving unified data management and flexible application.

[0003] The existing technology has the following shortcomings: In existing technologies, the dynamic data collection process for basic business source tables typically relies on timestamp chains to ensure the temporal continuity of data. However, due to the complexity of the collection environment, hidden breakpoints are prone to appear during the dynamic update process of the source tables. For example, data may be missing for certain periods due to network jitter, write latency, or system cache overflow. Such breakpoints are often difficult to identify directly from the surface structure. If these breakpoints are not traced during the source table component analysis stage, incomplete data sequences will be incorrectly solidified as continuous sequences during subsequent construction. The direct result is that the components cannot establish true continuous time series, causing the prediction model to malfunction within the critical window. This can lead to trend deviations or even model crashes, severely impacting data analysis and risk warning capabilities in dynamic business scenarios.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide a method for constructing structured and componentized basic business data tables to solve the problems mentioned in the background art.

[0006] To achieve the above objectives, the present invention provides the following technical solution: a method for structuring and componentizing a basic business data table, comprising the following steps: S1. Establish a unified time baseline and combine phase comb index and cross-source clock offset field to construct a time anchor group to provide an accurate benchmark for breakpoint identification. S2, under the constraint of the time-series anchor group, generates a breakpoint suspicion map, and constructs a breakpoint confidence matrix through mutual information gradient and arrival time residual, thereby accurately locating the gap; S3, based on the constraints of the breakpoint confidence matrix, obtains the causal playback chain, replays the time segment, calculates the gap width and drift direction, and then forms the repair prior; S4, with prior repair support, uses a multi-source aligner to align the collected logs and business event times, and performs interpolation reconstruction to finally output a continuous time series. S5, based on continuous time series, uses a purifier to construct a window guard through manifold filtering and consistency check to ensure series stability and effectively suppress spurious signals; S6 generates a control sequence under the constraints of the window guardrail, starts time inversion gating, drives the acquisition rhythm generator to inject supplementary acquisition instructions, backfills data in the neighborhood of the breakpoint, and updates the calibration matrix back to the unified time baseline.

[0007] Preferably, step S1 includes: To obtain a unified time baseline, a global reference time is set, and timestamps from multiple data sources are mapped to a unified time coordinate system. A standardized set of time segments is then constructed to establish the unified time baseline. Based on a unified time baseline, a phase comb index is obtained. By dividing the data into millisecond-level time slots and numbering them, the precise calibration of the time position of the data record is completed, and the continuity features are extracted for phase marking. Based on a unified time baseline and phase comb index, cross-source clock offset fields are obtained. By statistically modeling and vector correction of time offsets between different data sources, a stable time offset correction vector is generated. Based on the synergistic effect of a unified time baseline, phase comb index, and cross-source clock offset field, a time-series anchor group is established to locate a stable cluster of data records and form a highly consistent time-series reference unit.

[0008] Preferably, step S2 includes: Under the constraint of time-series anchor point group, obtain the breakpoint suspect map, analyze the change of anchor point density ratio through sliding time window, identify the region of sudden drop in anchor point density and extract it as the breakpoint suspect region. Within the suspected breakpoint region, mutual information structure analysis is performed, a joint distribution of temporal density is constructed and the mutual information gradient is calculated, and the time period of continuous decrease in mutual information is identified as the structural decoupling region. Based on the mutual information analysis results, the arrival time residual of data records within each time window is calculated, and the standard deviation of residual fluctuation is statistically analyzed to identify high residual regions with abrupt changes. By integrating anchor density, mutual information gradient, and time residual, a breakpoint confidence matrix is ​​constructed. After normalization, various indicators are weighted and merged to generate a breakpoint confidence score, which is used to calibrate the breakpoint location and form a breakpoint probability distribution.

[0009] Preferably, step S3 includes: Based on the time regions in the breakpoint confidence matrix where the confidence score is continuously higher than a preset threshold, key upstream and downstream time segments of the corresponding regions are extracted, and a breakpoint context sequence set is constructed to complete the establishment of the time projection sequence diagram. Based on the time projection sequence diagram, a time feature vector sequence is constructed, and the evolution trend of adjacent time slices in terms of density distribution, offset amplitude and time interval is analyzed to form a causal evolution path and construct a causal replay chain; Dynamic replay is performed according to the order of nodes in the causal replay chain. A replay behavior trajectory map is generated under a unified time baseline to identify gap candidate points and evaluate gap performance. Based on the abnormal sequence segments identified from the playback behavior trajectory map, the gap width is calculated and the drift direction is determined to form the basis for setting the supplementary sampling window and the interpolation accuracy control conditions. Based on the gap width, drift direction and behavior trajectory features, a repair prior structure is generated, and a repair condition parameter set including time index, recommended alignment granularity and credibility evaluation is constructed to support the next stage of data alignment and interpolation reconstruction operations.

[0010] Preferably, step S4 includes: Based on the prior information on the location of the interruption point, the drift direction, and the gap width, all data records from all sources are mapped to a unified time baseline, and cross-source record alignment and interpolation point identification are completed according to the recommended alignment granularity. Based on the aligned time position, combined with the interpolation method and granularity set in the repair prior, a time behavior slope evaluation model is constructed, dynamic density adjustment is performed on the interpolation region, and a sub-second level interpolation point set is generated and an identification attribute is attached. The original records, alignment correction records, and interpolation point sets are sorted and merged into a complete time series, labeled with source identification codes, record type labels, interpolation intensity identifiers, and repair segment numbers, and structural continuity detection and local re-interpolation processing are completed.

[0011] Preferably, the subsecond-level interpolation point set is dynamically adjusted based on the time behavior slope evaluation results during generation. When the time change trend is uniform, equal-interval interpolation is used, and when the time change trend fluctuates, the interpolation density is dynamically adjusted according to the recording interval to improve the continuity and structural stability of the interpolation results.

[0012] Preferably, step S5 includes: Based on the constructed continuous time series, interference window sets are identified and extracted according to the standard deviation of the recording time interval, the relative proportion of interpolation points, the consistency between the magnitude of the change in the time density gradient and the recording drift direction; The time records within the interference window set are subjected to quantile manifold filtering. Based on the division of quantile segments and the evaluation of behavior slope in the time-numerical space, extreme abnormal records are identified and replacement operations are performed. At the same time, time axis coherence constraints are applied to preserve the true behavior trajectory. A critical window guardrail structure is constructed based on the filtered interference window. A multi-boundary model is established by combining behavior offset bandwidth, time continuity score, interpolation point proportion intensity and behavior slope stability. Sub-window consistency check and boundary shrinkage operation are performed to finally generate a critical window guardrail with behavior perception capability.

[0013] Preferably, step S6 includes: Under the constraint of the critical window guardrail, the behavioral tension zone is extracted and the dynamic control area is screened by combining the residual offset index to establish a data window set for triggering supplementary sampling; A time inversion path is constructed on the data window set, the abrupt change location of the non-interpolated record is identified as the supplementary sampling injection point, and a set of acquisition instructions containing the acquisition time range, data source order and maximum number of supplementary samplings is generated. Based on the acquisition instruction set, pulse-level data acquisition and backfilling are performed on the neighborhood of the breakpoint, insertion judgment and replacement label update are performed, and sliding window difference calibration is used to ensure trajectory continuity; Extract the calibration performance parameters generated during the supplementary sampling process, including maximum backtracking length, number of samplings, interpolation coverage, sequence continuity score, behavioral consistency score, and repair impact magnitude. Construct a calibration matrix and write it back to a unified time baseline to complete the end-to-end correction closed loop.

[0014] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention centers on a unified time baseline and constructs a multi-dimensional time-series anchor point group to accurately identify and pinpoint data breakpoints. Then, based on a causal replay mechanism, it dynamically captures the behavioral characteristics of data gaps, forming prior conditions for repair. Building upon this, it employs multi-source alignment and sub-second-level interpolation strategies to reconstruct high-precision continuous time series. Furthermore, it constructs a critical window barrier through manifold filtering and consistency checks to effectively suppress residual pseudo-signals and local disturbances. Finally, through a time inversion-driven mechanism and supplementary data acquisition, it achieves pulse-level repair of the breakpoint region and writes calibration information back to the time baseline, realizing adaptive correction and dynamic control of the entire time structure. This solution not only significantly improves the structural integrity and temporal stability of source table components but also enhances the robustness of the data system, the reliability of the model, and the real-time performance of early warnings in dynamic business scenarios. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0016] Figure 1 This is a flowchart illustrating a method for constructing a basic business data table using structured and componentized methods according to the present invention. Detailed Implementation

[0017] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0018] This invention provides, for example Figure 1 The method for structuring and componentizing a basic business data table, as shown, includes the following steps: S1. Establish a unified time baseline and combine phase comb index and cross-source clock offset field to construct a time anchor group to provide an accurate benchmark for breakpoint identification. This step proposes a method for constructing a time series anchor group based on a unified time baseline, phase comb index, and cross-source clock offset field. This method supports the identification and tracing of data time series breakpoints, improving time consistency and integrity in multi-source data processing. The specific steps are as follows: To construct a unified time baseline, data records containing timestamp fields need to be extracted from multiple heterogeneous data sources and uniformly standardized in time. Specifically, a global reference time is first established, using a highly stable time signal source, such as the Network Time Protocol (NTP) service or the Global Positioning System (GPS) timing signal, as the base time source. Then, the timestamps of each data source are converted to map them to this unified reference time coordinate system. During the timestamp conversion process, obviously abnormal time values ​​should be identified and removed, such as records earlier than the system startup time or later than the current time. Next, all timestamps from each data source are divided into time segments at fixed time intervals to generate a set of time segments. The first and last times, median time, and number of records in each time segment are statistically analyzed to determine the temporal distribution characteristics of the data. Based on this, the time segments of data from different sources are cross-referenced to analyze their overlap and misalignment in time. A mapping function is constructed based on the statistical characteristics to align all data to the same time axis, thus forming a unified time baseline. This baseline can support precise alignment at the millisecond level, adapting to the processing needs of high-frequency business data.

[0019] After establishing a unified time baseline, a millisecond-level phase comb index needs to be constructed to further enhance the ability to identify time series structures. The specific implementation is as follows: using the unified time baseline as a reference, it is divided into equally spaced millisecond time slots, ensuring that the start and end boundaries of each time slot do not overlap, and a one-to-one sequence number is established. Then, all data records are traversed, and their time slots are determined based on their timestamp values, mapping them to the corresponding time slot numbers to complete the initial index establishment. Further, feature analysis is performed on the data records within each time slot, including calculating parameters such as the variance of time intervals between records, record frequency, continuity score, and temporal density gradient. Based on these parameters, features such as local clustering, intermittent interruptions, and nonlinear time drift in the data rhythm are identified, and these temporal characteristics are marked using phase labels. In areas with dense records but short time spans, sub-millisecond-level indexes are used to refine the time slot division, and high-precision sequence numbers are added to prevent data overlap or order disorder. Finally, through full scanning and index annotation, a complete phase comb index sequence is generated, ensuring that the position of all data records in the time series can be accurately identified and reconstructed.

[0020] To address time discrepancies between multiple data sources, a cross-source clock offset field needs to be constructed to quantify and vectorize the time offset between sources. The specific steps include: First, aggregate and statistically analyze records from each data source sequentially, defining multiple time windows of equal length under a unified time baseline, and calculating the median, variance, and peak position of the time distribution of data within each window. Then, select combinations of sources with high temporal overlap, calculate the offset difference between the medians of these combinations within each window, and record the offset direction and magnitude. Construct a time offset trajectory curve from the set of offset differences across all windows, and fit it to obtain a preliminary time offset model. Based on the offset model, a residual analysis mechanism is introduced to smooth the residuals between the model and the actual data distribution using a moving average, generating an offset correction vector for each time node. To ensure the stability of the correction results, cross-validation is performed under multi-window conditions, eliminating abnormal windows with variances exceeding a threshold. Finally, a continuous cross-source clock offset field is constructed, assigning a timing correction value to each time node for subsequent time synchronization and data record position repair operations, achieving fine-grained correction of time sequences between multiple sources.

[0021] Table 1: Definition of Time Overlap and Source Combination;

[0022] This describes in detail how to quantify: the degree of temporal overlap, how to define a source combination, and provides criteria for determining the number of windows.

[0023] Time overlap (%): Calculate the time overlap ratio between windows, for example, divide the overlap time of two windows by the total time length of the two windows to obtain the degree of overlap.

[0024] Source combination: A combination of multiple time windows, which is classified into the same source combination based on the degree of overlap.

[0025] Processing methods: For combined or truncated windows with low overlap, explain how to merge or discard data.

[0026] This table shows how to define source combinations based on the degree of overlap between time windows. Windows with high overlap are grouped into the same combination, while windows with low overlap can be processed by merging, discarding, or boundary adjustment.

[0027] Table 2: Calculation table of cross-source clock offset field correction values;

[0028] This table describes the calculation process for the cross-source clock offset field, including how to assign correction values ​​to each time point and how to modify the timestamps based on the data arrival time distribution.

[0029] Original timestamp value: The original timestamp from the data source.

[0030] Corrected offset value: A correction value calculated based on time overlap and time distribution from other data sources.

[0031] Corrected timestamp: The corrected timestamp is obtained by adding the correction offset value to the original timestamp value.

[0032] Offset direction: Indicates whether the correction is forward (positive) or backward (negative).

[0033] Calibration accuracy: The accuracy of the offset correction is evaluated based on the degree of agreement between the corrected data and the actual data.

[0034] This table clearly shows how to calculate the correction value for each data source and adjust the timestamps. Based on the distribution of timestamps, the offset direction and accuracy of each data source are evaluated, thereby performing fine-grained time-series corrections on the data.

[0035] By integrating the results of a unified time baseline, phase comb index, and cross-source clock offset fields, a time-series reliable anchor point group is established. In the specific implementation, firstly, using the unified time baseline as the horizontal axis and the phase comb index number as the vertical axis, each data record corrected by the offset field is positioned as a spatial coordinate point. Next, a sliding mechanism with a fixed window is used to analyze this spatial distribution, identifying data clusters with high-density clustering characteristics within continuous time periods. For each candidate data cluster, the number of records on its time axis, the distribution density of index numbers, and the consistency of offset correction values ​​are calculated to form a clustering strength score. Only data clusters with scores higher than a set threshold are retained as candidate reliable anchor points. Further structural stability checks are performed on this candidate set, calculating its time-series gradient stationarity, record distribution uniformity, and index number continuity. The set of data clusters that meet the stability conditions is formally defined as the time-series reliable anchor point group. This anchor point group possesses high consistency and stability across sources, time periods, and frequencies, making it an irreplaceable time-series reference unit for subsequent breakpoint identification and data integrity verification.

[0036] Table 3: Calculation table for cluster strength score of credible anchor points;

[0037] This table describes how cluster strength scores are calculated and explains the definition of stability conditions. The stability of data clusters is calculated using scores across multiple dimensions.

[0038] Time density: The number of data records per second, reflecting the density of the data.

[0039] Offset correction value consistency: The consistency of timestamp correction values ​​within the dataset; the smaller the standard deviation, the higher the consistency.

[0040] Cluster strength score: The cluster strength value is calculated based on time density and consistency score. The higher the score, the more stable the data cluster.

[0041] Stability conditions: Stability conditions may include the standard deviation of the time interval of a data cluster, the record density, etc. A data cluster that meets the conditions can be considered a trusted anchor group.

[0042] This table demonstrates how to calculate cluster strength scores across multiple dimensions to identify and define trustworthy time-series anchor clusters, and provides stability conditions to determine which data clusters meet the criteria for trustworthy anchor clusters.

[0043] S2, under the constraint of the time-series anchor group, generates a breakpoint suspicion map, and constructs a breakpoint confidence matrix through mutual information gradient and arrival time residual, thereby accurately locating the gap; After the establishment of the time-series trusted anchor group, in order to further identify potential breakpoints in the data sequence and determine the location of gaps, the following steps are taken: extraction of suspected breakpoint regions, mutual information structure analysis, time residual calculation, and generation of breakpoint confidence matrices. The specific implementation steps are as follows: Based on the established time-series reliable anchor point group, suspected breakpoint regions are extracted. During implementation, each data record, after clock offset correction and with a phase comb index, is fully mapped on a unified time baseline to construct a complete time-series projection map. Continuous, equal-length sliding time windows are set on this time series, for example, each window is 10 seconds long, sliding sequentially according to time. For each sliding window, the number of anchor point records within its coverage area is counted, and the ratio of the number of anchor points in two adjacent windows is calculated to construct an anchor point density change curve. When the density ratio of two or more consecutive windows shows a significant decrease, and the decrease ratio is greater than the set density change threshold, the region is marked as a candidate region for anchor point structural breakage. Simultaneously, to avoid misjudging local anchor point fluctuations as structural breakpoints, a cross-window smoothing judgment mechanism is set to analyze whether the breakage trend is persistent across multiple windows. When the above conditions are met, the candidate region is included in the breakpoint suspicion map as a key object for subsequent analysis.

[0044] Within the time region defined by the breakpoint suspicion map, mutual information structure analysis is conducted to reveal whether the data structure dependencies between time periods have been disrupted. In this process, each pair of continuous sliding windows is selected, and the data record sets within them are extracted. Density distribution maps of each set are constructed on the time axis. Subsequently, the two distribution maps are jointly statistically analyzed according to the time scale to construct a joint probability distribution map, and the mutual information value between the two windows is calculated, reflecting their degree of correlation in the time structure. The mutual information values ​​of all continuous window pairs are arranged to form a mutual information curve, and its first derivative, i.e., the mutual information gradient sequence, is calculated. When a continuous negative gradient appears in a certain time period, and the gradient descent magnitude is greater than a preset structural change threshold, the corresponding location is determined to be a structural decoupling region, which may indicate record discontinuities, temporal disconnections, or event gaps. To eliminate misjudgments caused by occasional fluctuations, a moving average process is further introduced to smooth the gradient sequence, remove high-frequency disturbance signals, and retain only the continuous structural trend as the breakpoint boundary marker.

[0045] Mutual information analysis is only performed on data record sequences that are within the same basic business source table component or that are confirmed to have the same source business attributes through field mapping. The data records originate from the same business event type or a set of fields with equivalent time behavior characteristics.

[0046] Specifically, the adjacent time windows selected in the breakpoint suspect graph should meet the following requirements: their corresponding data records belong to the same source table component, or belong to the equivalent field set that has established a mapping relationship during the component sorting process, so as to ensure that the data participating in the mutual information calculation are comparable in terms of business structure.

[0047] Mutual information is used to characterize the temporal structural dependencies between the arrival time distributions of data within adjacent time windows, reflecting the continuity and coupling of data streams on the time axis, rather than to measure the correlation between data business semantics.

[0048] When performing mutual information calculation, the original business values ​​are not used directly. Instead, the data records in each time window are mapped to time structure feature vectors. The time structure feature vectors include at least one or more of the following: record arrival time series, time interval distribution between records, time density histogram or phase slot occupancy probability distribution. Then, a joint probability distribution is constructed based on the above time structure feature vectors and the mutual information value is calculated.

[0049] Based on mutual information structure analysis, a time residual index is introduced to improve the numerical stability and intuitive interpretability of breakpoint boundary identification. In practice, for each sliding window to be tested, all data records are extracted, and the time offset of these records' timestamps relative to the median time of the previous window is calculated. This offset value represents the time deviation between the record's arrival time and the structural center of the previous time period, serving as a direct parameter for measuring temporal continuity. All offset values ​​are plotted as a time residual curve in chronological order, and its trend is observed. When a significant abrupt change occurs in the residual curve within a certain time period, and the fluctuation amplitude continuously exceeds a preset residual anomaly threshold, it indicates that the data flow exhibits unnatural jump behavior in that region. To ensure accurate judgment results, a piecewise standard deviation statistical method is introduced to measure the piecewise fluctuation of the residual change rate. Only when the standard deviation exceeds the threshold for more than two consecutive time periods is the region confirmed as a candidate breakpoint region with high residual anomaly characteristics.

[0050] A breakpoint confidence matrix is ​​generated by fusing breakpoint probabilities, mutual information gradient sequences, and temporal residual curves to quantify breakpoint locations. First, a corresponding record row is created for each time scale, and each row is filled with three types of indicators for that time point: anchor density ratio, mutual information gradient value, and temporal residual amplitude. These three indicators are then normalized to ensure their values ​​are on a uniform scale. Next, the data points for each time scale are weighted and merged according to preset weights: 30% for anchor sparsity, 40% for mutual information gradient, and 30% for temporal residual, generating the final breakpoint confidence score. The scores for all time points are plotted as breakpoint confidence curves, and time points with scores exceeding a confidence threshold are clustered into confidence breakpoint fragment regions. The resulting breakpoint confidence matrix, using time points as indices and multi-dimensional indicator fusion scores as values, can accurately represent the probability intensity distribution of breakpoint regions, providing data input and location guidance for subsequent gap playback and repair. This method integrates structural information, residual fluctuations, and data density in multiple dimensions, and outperforms traditional methods that rely on a single threshold in terms of recognition ability, anti-interference ability, and quantization ability.

[0051] S3, based on the constraints of the breakpoint confidence matrix, obtains the causal playback chain, replays the time segment, calculates the gap width and drift direction, and then forms the repair prior; After constructing the breakpoint confidence matrix and identifying potential breakpoint regions with high confidence, to achieve breakpoint attribution analysis and gap quantification, it is necessary to construct a causal replay chain and form repair prior conditions to support the accurate execution of the subsequent alignment process. The specific steps are as follows: Within a time region where the confidence score identified in the breakpoint confidence matrix is ​​continuously higher than a set threshold, key time-series segments upstream and downstream of this region are extracted as the behavioral tracing range. To achieve fine-grained localization, a fixed time span is extended forward and backward from the breakpoint region, for example, extracting data records within 30 seconds forward and backward, forming a breakpoint context sequence set. Within this interval, all data records are rearranged according to their timestamp order and normalized using a unified time baseline to establish a time projection sequence diagram of the original data. This sequence diagram is initially segmented into multiple segments of equal length, for example, each segment is five seconds long and numbered sequentially. For each time segment, the number of records, time density, minimum time difference between records, degree of temporal distribution skewness, and frequency of phase index changes are statistically analyzed to form a multi-dimensional temporal feature vector sequence, which serves as the structural basis for the subsequent generation of causal replay chains.

[0052] Based on the temporal feature vector sequence, a causal evolution path is constructed, forming a causal replay chain. During the operation, starting from the time slice before the breakpoint, the continuity in density distribution, offset amplitude, and time interval patterns between it and the next time slice is analyzed sequentially. If adjacent time slices show a decreasing trend in temporal density, a cumulative increase in offset amplitude, and a gradually lengthening time interval between records, it can be preliminarily identified as a potential causal decay path. This path is continued to be tracked until a structural abrupt change occurs in the indicators, such as a rapid rebound in temporal density or a sudden negative offset value, which is considered the end of the replay path. The time slices after the breakpoint are analyzed simultaneously to determine whether behaviors such as temporal density jumps, offset direction reversals, and phase index jumps have anti-causal characteristics. These behavioral nodes are connected through a directed path structure to form a complete causal replay chain from before the breakpoint to after the breakpoint. Each node in the chain contains a complete time label, structural indicators, and behavioral change identifiers. This causal chain reflects the break pattern of data behavior on the time axis, significantly improving the ability to reconstruct the true evolution process of temporal behavior compared to traditional static time range truncation methods.

[0053] Dynamic replay is performed based on the node order of the causal replay chain, and gap performance is evaluated during the simulation. Under a unified time baseline, the original data is reloaded according to the node time order in the replay chain, and a simulated time progression trajectory is constructed. During the simulation, each time segment is activated sequentially according to the time interval of the original record in the chain, and its replay position on the time baseline is recorded. Through this simulated replay behavior, a replay behavior trajectory map is generated, which is compared with the actual distribution trajectory of the anchor point group. The time position difference, recording trigger order error, phase misalignment frequency, and time slice loss rate of each time point are statistically analyzed. Based on this, the locations where data discontinuity, replay chain break, or logical interruption occurs in the simulated replay are identified and classified as valid gap candidate points. At the same time, the sum of residuals, peak residuals, and duration of continuous residuals in the simulated replay are recorded, providing a numerical basis for gap quantification. This replay not only restores the real time structure misalignment process at the breakpoint but also provides a dynamic basis for gap measurement in terms of time behavior dimension.

[0054] Based on the abnormal sequence segments generated during dynamic playback, the width of the gap segment and the drift direction are determined. The gap width is determined by identifying the time range of continuous data interruption, i.e., the total duration covered by the time period when no record appears consecutively in the simulated playback chain. Specifically, the calculation method is to locate the time jump between two consecutive valid records in the playback trajectory and take the largest jump segment as the gap width. Simultaneously, the drift direction is determined by comparing the time offset trends of records before and after the gap: if the record before the breakpoint is triggered prematurely in playback and the record after the breakpoint is significantly delayed, it is determined to be a time-back drift; otherwise, it is a time-front drift. Furthermore, if there is an overlapping change in the time direction between the records before and after the gap, i.e., the former segment is delayed and the latter segment appears on the time axis before the breakpoint, it is identified as a bidirectional misalignment drift. All gap width values ​​and drift characteristics will serve as core parameters for repair priors, driving subsequent settings for supplementary sampling windows, interpolation granularity selection, and time alignment accuracy adjustment.

[0055] During the causal playback process, for the valid records adjacent to the gap, the time offset residual between the playback time in the playback trajectory and the corresponding time anchor point time under the unified time baseline is calculated, where the time offset residual is the playback time minus the corresponding anchor point time.

[0056] When the time residual corresponding to the record before the gap is negative and the time residual corresponding to the record after the gap is positive, it indicates that the data at the gap position has changed from being triggered in advance to arriving late, and the gap is determined to be time-drifted. When the time residual corresponding to the record before the gap is positive and the time residual corresponding to the record after the gap is negative, the gap is determined to be a time drift. When the signs of the time residuals recorded before and after the gap are inconsistent and the residual amplitudes change crosswise, it is determined to be a two-way misalignment drift.

[0057] The drift direction serves as an important prior parameter for gap repair, and is used for subsequent interpolation direction selection and supplementary sampling strategy setting.

[0058] Table 4: Determination Table of Gap Width and Drift Direction;

[0059] This table describes how to calculate the gap width and drift direction based on the playback behavior trajectory.

[0060] Gap Pre-Timestamp: The last valid timestamp before the gap.

[0061] Post-gap timestamp: The first valid timestamp after the gap.

[0062] Maximum time difference: The maximum time difference between the timestamps before and after the gap.

[0063] Drift direction: The drift direction is determined by comparing the time offset changes recorded before and after the gap, indicating whether the drift is forward or backward.

[0064] Gap width: The duration of the gap, i.e. the maximum time difference.

[0065] This table is used to determine the width and drift direction of the gap, help determine how to perform supplementary sampling, and further guide the data repair process.

[0066] Based on the replay behavior trajectory diagram, gap width quantification index, and drift direction characteristics generated in the aforementioned steps, a repair prior structure is generated and transformed into a set of repair condition parameters that can be directly invoked during the data alignment stage. The repair prior structure uses the start and end times of the breakpoint within the unified time baseline as the main index, and includes the gap width, recommended alignment window length, preferred interpolation method order, and directional suggestions for re-collection. The repair prior structure is generated through rule mapping of the gap width, drift direction, and time residual evaluation index obtained during causal replay. Specifically, the gap width is used to determine the recommended time alignment window length; the drift direction is used to determine the time progression direction of interpolation reconstruction and the priority order of re-collection; and the time residual evaluation index is used to assign credibility weights to the repair operation. Each parameter is combined according to preset mapping rules to form structured repair prior entries. These entries use the start and end times of the breakpoint within the unified time baseline as the main index, and include the corresponding alignment granularity, interpolation method identifier, and re-collection direction identifier. The repair condition parameter set is generated by expanding the repair prior structure. Each breakpoint corresponds to at least one repair parameter entry, which is stored in a structured form and serves as direct input to the subsequent multi-source alignment and re-acquisition modules. During the generation of the repair prior structure, multiple breakpoints are sorted chronologically, and the time interval between adjacent breakpoints is calculated. When repair windows corresponding to adjacent breakpoints overlap, repair task conflicts are avoided by merging repair windows or adjusting repair priorities. The repair priority is determined based on the breakpoint confidence score and the size of the playback residual. The prior structure also includes residual evaluation indicators from playback behavior as a confidence benchmark for assessing repair feasibility. During the generation of the repair prior structure, cross-analysis of the intervals and mutual influence relationships between different breakpoints is also required to avoid data processing conflicts caused by overlapping repair tasks. The final generated repair prior parameter set will serve as the input standard for the next stage of data alignment and reconstruction interpolation, controlling the sensitivity of time adjustment, the synchronization of re-acquisition directions, and the adaptation range of interpolation boundaries. This method effectively overcomes the problems of rigid repair strategies, interpolation distortion, and delayed data collection in traditional solutions by establishing a logical closed loop between causal behavior and data repair.

[0067] Table 5: Repair Condition Parameter Set Generation Table;

[0068] The table lists in detail the parameters in the repair prior structure and describes how to generate a set of repair condition parameters from different conditions.

[0069] Gap width: The duration of the gap.

[0070] Alignment window length: The length of the time alignment window used during repair.

[0071] Interpolation method: The interpolation method used (such as linear interpolation, quadratic interpolation, etc.).

[0072] Supplementary sampling direction: The direction of the data during supplementary sampling (forward or reverse).

[0073] Residual evaluation index: The index for evaluating the residuals after repair.

[0074] Repair condition parameters: Repair conditions generated based on various indicators help determine whether repair needs to be performed.

[0075] This table helps to clearly show how to generate repair conditions based on parameters such as the gap width and the alignment window length, and decide whether to perform the repair operation.

[0076] S4, with prior repair support, uses a multi-source aligner to align the collected logs and business event times, and performs interpolation reconstruction to finally output a continuous time series. After generating the prior structure for repair, fine alignment of the multi-source time data is required based on the prior parameters, followed by sub-second interpolation reconstruction to form a continuous, usable, high-precision time series, providing structurally complete input for subsequent data cleansing stages. The specific steps are as follows: Based on the breakpoint locations, drift directions, gap widths, and recommended alignment granularity specified in the repair prior, time coordinate mapping under a unified time baseline is performed on data records from all sources, followed by alignment processing. Specifically, the original records from each source are reordered according to their acquisition time sequence, and the timestamp field of each record is converted to the time coordinate format defined in the unified time baseline. After baseline mapping, records within the gap's vicinity are selected based on the breakpoint start and end times recorded in the repair prior. The selected results are marked with the record's source, original offset direction, time drift degree, and whether it is near the interpolation boundary. Under the unified time baseline, a target alignment reference point set is defined by the interpolation step size provided in the repair prior, for example, using 10 milliseconds as a time node for alignment comparison. For each node in the target reference point set, a cross-source scan is performed to determine if there are at least two valid records from different sources at that time node. If a source is missing, its target insertion position is estimated based on the temporal position of its original data at adjacent reference points. Before interpolation, all records from the source are fine-tuned to precisely align their timestamps to the target reference point. For records with significant deviations, timeline interpolation prediction is performed, inserting them in the middle between the preceding and following valid records and assigning them an interpolation identifier. Through this method, time records from different sources achieve positional alignment and time logic correction on a unified timeline, avoiding alignment misjudgments caused by accumulated time drift in traditional techniques.

[0077] After completing time alignment and identifying the interpolation region, subsecond-level time interpolation reconstruction is performed based on the interpolation method and granularity defined in the repair prior. Specifically, for each consecutive valid aligned record, the actual time interval is first calculated and compared with the maximum acceptable time jump suggested in the repair prior. If the interval exceeds the suggested value, the segment is designated as an interpolation segment. Within the interpolation segment, a time behavior slope evaluation model is constructed based on the timestamps, record density, record sequence position difference, and time change trend between the two points on both sides of the interpolation boundary. This model is used to determine whether there is a linear time evolution trend or time behavior fluctuations within the segment. If the behavior trend is continuous and uniform, interpolation is performed with a fixed step size, scaled proportionally to the time interval between preceding and following records. If the trend fluctuates, a dynamic interpolation density adjustment method is used, i.e., a 1-millisecond interval is used for shorter intervals, and a 5-millisecond interval is used for longer intervals. After each interpolation point is generated, a timestamp, original reference source, interpolation source weight, and corresponding repair segment marker are immediately appended to it. All interpolation points constitute the complete time band of the repair section, ensuring that the data is unbroken, drift-free, and out of order on the time axis. Compared to the traditional method of using only a fixed time step for equidistant point filling, this implementation combines dynamic step granularity with repair priors to effectively prevent data structure distortion caused by time interpolation.

[0078] After completing the interpolation reconstruction of all time points and generating the interpolation point set, all processed data needs to be reorganized into a continuous and complete time series structure to provide standardized input for subsequent purification processing stages. The operation process is as follows: all original records from all sources, aligned and corrected records, and newly generated interpolation points are merged into a unified data set and sorted in ascending order according to a unified time baseline to ensure the strict order of records at each time point. Four types of identity attributes are attached to each record: first, a data source identification code to indicate the record's source location; second, a record type label to identify whether it is an original record, an aligned record, or an interpolated record; third, an interpolation identifier strength to indicate the trust level of the interpolation point, such as fully predicted generation, extrapolation based on the repair trajectory, or construction based on the average of adjacent records; and fourth, a repair segment number to indicate the repair range of the record and its corresponding playback residual level. On the completed time series structure, a full sequence coherence check is performed, including determining whether the time jump between any two consecutive records exceeds a set threshold, whether there is duplicate record insertion, and whether there are abnormal structures such as order reversal. If the detection results meet the continuity requirements, the time series is passed to the next stage of processing as the output structure. If there are jump anomalies, the corresponding regions in the prior are backtracked and repaired, the alignment density and interpolation accuracy are re-evaluated, and local re-interpolation is performed until the structural coherence requirements are met. The final output time series not only has high temporal accuracy, but also source transparency, interpolation interpretability, and breakpoint location traceability, which is an important data foundation for subsequent critical window purification and dynamic control execution.

[0079] S5, based on continuous time series, uses a purifier to construct a window guard through manifold filtering and consistency check to ensure series stability and effectively suppress spurious signals; After constructing a continuous time series with a unified time baseline, accurate interpolation completion, and time alignment in the previous step, it is necessary to further eliminate potential spurious signals, suppress residual disturbances, and establish behavioral boundary protection structures for key regions to ensure sequence stability and the security of subsequent dynamic operations. The specific steps are as follows: This study identifies and extracts key regions in continuous time series with residual perturbation risks, constructing an operable set of interference windows. Specifically, the entire time series is divided into non-overlapping time partitions, for example, time slices every 20 seconds. For each slice, four indicators are calculated: the standard deviation of the recording time interval, the relative proportion of interpolation points, the magnitude of the temporal density gradient change, and the degree of inconsistency in the time drift direction of adjacent records. These four indicators are weighted and combined to generate a stability score for that time slice. Time segments with scores below a set threshold are screened through a full-sequence scan, and further superimposed with breakpoint reconstruction regions and densely interpolated regions from the repair prior, obtaining precisely overlapping interference segments. Regional structural stability analysis is performed on these regions, focusing on identifying areas where the recording density suddenly becomes sparse, interpolation points show concentrated stacking, or there are local abrupt jumps. All time segments that meet the interference characteristic judgment criteria are uniformly included in the interference window set as the processing targets for subsequent filtering operations. This identification strategy differs from existing technologies that only use sliding standard deviation to identify outliers; by employing joint indicator structure analysis, it can more effectively identify regions of implicit data distortion introduced by repair.

[0080] Overlay refers to performing an intersection operation on the time axis on the reconstructed segment of the breakpoint marked in the repair prior and the interpolation-dense segment to form a candidate time segment that simultaneously satisfies the breakpoint repair feature and the interpolation-dense feature, which serves as the precise interference segment.

[0081] A sudden sparseness in record density does not mean that the original breakpoints have not been repaired, but rather that after interpolation reconstruction and outlier removal, the number of valid records in a local time segment is lower than the overall average level. This situation may be caused by adjustments to the interpolation granularity or correction of local anomalies.

[0082] Regional structural stability analysis is achieved by comprehensively evaluating the standard deviation of time intervals, the rate of change of interpolation point proportions, and the fluctuation amplitude of time density gradient within candidate interference segments. When the above indicators exceed the preset threshold, the segment is determined to be an abnormal distortion area and is included in the subsequent filtering and guardrail construction process.

[0083] A fine-grained filtering operation based on quantile manifold structure constraints is performed on the data within the interference window set to suppress extreme offset points, weaken local jumps, and preserve the true behavioral trajectory characteristics. The specific steps are as follows: Records within the interference window are mapped to a two-dimensional time-value space. In this space, quantile boundaries for each time period are generated according to the recorded values, for example, set as five quantile segments: low value zone, low-middle zone, median zone, high-middle zone, and high value zone. Next, the quantile segment to which each record belongs is determined, combined with its time position, interpolation label, and the difference ratio with its two neighboring points. For records located in the extreme regions of high or low quantiles, if their time interval is abnormal, originates from interpolation construction, and the difference with adjacent records exceeds twice the mean of the window, they are identified as high-risk anomalies. A replacement operation is performed on these points, using the mean of the two records to their left and right or the median of the neighboring sub-segments for interpolation replacement. Simultaneously, manifold smoothing constraints are applied on the time axis: limiting the sliding of record value changes within consecutive quantile intervals before and after them, prohibiting jumps in time points to intervals differing by more than two quantile intervals. For recorded trajectories that violate this condition, adjustments are made by back-calculating the recorded values ​​using the average change gradient of neighboring trajectories to ensure logical consistency of value changes over time. After filtering, all points involved in the processing are marked with a label field, preserving the processing basis and alternative methods to provide input for subsequent guardrail calculations. This method, by constructing a quantile-level filtering channel, effectively suppresses spurious signals caused by interpolation stacking, error accumulation, or boundary interference while preserving true fluctuation behavior.

[0084] After filtering and purification, a critical window guardrail structure is constructed based on the interference window set, and local consistency verification is performed to establish the temporal behavior boundary and prevent overreaction during future prediction calls. Specifically, within each interference window, the value trajectory sequence of the filtered records is extracted, and the behavior offset bandwidth of that segment is calculated, i.e., the difference between the maximum amplitude of the recorded value change and the average gradient. Simultaneously, three indicators are extracted within the same segment: the continuity score of the time distribution, the intensity of the interpolation point proportion, and the stability of the behavior slope. These four indicators are used as input parameters for the guardrail boundary model, and a weighted calculation is performed to generate the upper boundary line (maximum permissible offset), the lower boundary line (minimum permissible offset), and the behavior centerline (normal trend path). Based on these three boundary lines, a critical window guardrail is established within this time period. This guardrail will be used in the future data-driven phase to limit prediction input, control reverse sampling triggering, and weaken pseudo-trend amplification. To prevent overprotection from causing a suppressive effect, a local consistency check is performed within each guardrail. The area is divided into sub-windows every two seconds, and the data gradient within each sub-window is assessed for continuity, unidirectional progression of numerical changes, and multiple reverse bounces. For sub-windows that do not meet the consistency criteria, a boundary contraction operation is performed to reduce the allowable behavioral drift range in that area, and the central axis direction is recalibrated to ensure logical coherence and data stability of the overall structure. The resulting critical window guardrail structure possesses multi-layered boundary, multi-indicator control, and behavioral perception capabilities. It effectively isolates pseudo-signal fluctuations while preserving key business trend changes, serving as a crucial structural guarantee for connecting prediction logic with real-time data interaction.

[0085] S6, under the constraints of the window guardrail, generates the control sequence, starts the time inversion gating, drives the acquisition rhythm generator to inject supplementary acquisition instructions, backfills data in the neighborhood of the breakpoint, and updates the calibration matrix back to the unified time baseline; After constructing a time series with continuity and behavioral stability, it is necessary to further identify and repair any remaining hidden breakpoints and drift risks. Through a time-reversal-driven mechanism and a pulse-level data recovery method, end-to-end correction and adaptive closed-loop control of the dynamic data link are achieved. The specific steps are as follows: Under the constraints of the established critical window barriers, candidate time segments for dynamic control are identified. During the operation, all constructed critical window barriers are scanned one by one, and the upper boundary line, lower boundary line, and behavioral trend centerline defined by each barrier structure are extracted. Based on these boundary parameters, time behavior tension zones are constructed. Further, residual offset indices are calculated for each time point within each tension zone, including: whether historical interpolation positions are concentrated, the magnitude of numerical jumps between preceding and following data points, the cumulative offset between the current time period and the time baseline, and the consistency score of the residual direction. If a segment exhibits drastic data fluctuations, concentrated jumps, or abnormal directional drift in these indices, that segment is identified as a high-priority dynamic control area. To ensure the targeting and continuity of subsequent operations, the time interval between the segment and the previous repair operation, and the degree of overlap between preceding and following data reconstruction behaviors, are also confirmed. Finally, a data window set suitable for performing reverse injection supplementary sampling is selected as the target area for triggering the subsequent time inversion gating mechanism.

[0086] After the target control area is clearly defined, a time-reversal gating mechanism is activated, and a reverse acquisition path is constructed by combining the original time sequence in the unified time baseline. Specifically, the system needs to search for its preceding records in reverse order in the time series based on the starting time point of each control window, and gradually backtrack to construct a time-reversal chain. At each backtracking time point, the time interval between it and the current point, the continuity of the record, whether it is interpolated data, and the trend of change in the numerical direction with adjacent points need to be calculated. If, during the backtracking process, a situation is found where there is a continuous non-interpolated record but a sharp jump in value change between any two adjacent time points, it is identified as a critical breakpoint for triggering supplementary acquisition. This position serves as the injection point in the time-reversal path. Subsequently, supplementary acquisition execution parameters are generated for this injection point, including: the time range of the acquisition start and end points, the time interval of each acquisition operation, the priority order of data sources for acquisition, and the maximum number of supplementary acquisitions threshold. These parameters constitute a rhythmic acquisition instruction set and are transmitted to the acquisition execution control unit to drive subsequent periodic data supplementary acquisition operations.

[0087] Driven by acquisition control logic, data in the vicinity of breakpoints is acquired using a pulse-level acquisition method, and a reverse data channel is constructed to achieve high-precision data backfilling. The operation process is as follows: according to the acquisition time window specified in the previous step, acquisition pulses are released at each acquisition time point, and historical reconstructable data is extracted from the specified data source. The acquisition results undergo real-time quality verification, including: the integrity of the data timestamps, the consistency of the source data, and whether the change gradient matches that of adjacent points. For data that passes verification, it is inserted into the target position of the original time series, and it is determined whether it overlaps with existing interpolated data. If overlap exists, the system will select and retain higher-quality data based on data integrity, timestamp accuracy, and the similarity of the change trend, while updating the replacement identifier of the interpolated record. After insertion, at least two original data points are selected before and after the acquisition area, and sliding window difference calibration is performed to ensure a smooth transition in numerical trajectory and time trend between the newly inserted data and neighboring records. The entire backfilling process requires recording the data source index, acquisition delay, insertion position, coverage replacement behavior, and trend changes before and after correction, serving as the basis for subsequent end-to-end calibration.

[0088] After completing the data backfilling operation, a calibration matrix needs to be constructed and written back to the unified time baseline to achieve a complete correction loop from identification, inversion, injection, supplementary sampling, backfilling to final structural update. The calibration matrix is ​​constructed on a per-supplementary-sampling-point basis, extracting six dimensions: maximum backtracking length of the reverse path, number of backfilling attempts, interpolation coverage of the resampled data, continuity score of the backfilled sequence, behavioral consistency score, and impact on the previous repair operation. These indicators are organized into a six-column structured form, with each row corresponding to a complete behavioral record of a supplementary sampling operation. After the calibration matrix is ​​constructed, the corresponding time period in the unified time baseline is located according to its index. A new benchmark marker is inserted at that position, the calibration weight factor is updated, and the structural trend after this round of supplementary sampling is appended to the trend template of the time baseline. This process not only performs adaptive structural updates for the current time period but also establishes a more accurate data input foundation for future prediction modeling and dynamic response.

[0089] This invention centers on a unified time baseline and constructs a multi-dimensional time-series anchor point group to accurately identify and pinpoint data breakpoints. Then, based on a causal replay mechanism, it dynamically captures the behavioral characteristics of data gaps, forming prior conditions for repair. Building upon this, it employs multi-source alignment and sub-second-level interpolation strategies to reconstruct high-precision continuous time series. Furthermore, it constructs a critical window barrier through manifold filtering and consistency checks to effectively suppress residual pseudo-signals and local disturbances. Finally, through a time inversion-driven mechanism and supplementary data acquisition, it achieves pulse-level repair of the breakpoint region and writes calibration information back to the time baseline, realizing adaptive correction and dynamic control of the entire time structure. This solution not only significantly improves the structural integrity and temporal stability of source table components but also enhances the robustness of the data system, the reliability of the model, and the real-time performance of early warnings in dynamic business scenarios.

[0090] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for organizing and constructing basic business source table components, characterized in that, Includes the following steps: S1. Establish a unified time baseline and, in conjunction with phase comb indexing and cross-source clock offset field, construct a time series anchor group; The phase comb index is constructed as follows: Millisecond time slots are divided at equal intervals using a unified time baseline as a reference, and corresponding numbers are established; data records containing timestamp fields are extracted from multiple heterogeneous data sources, and these records are mapped to their corresponding time slot numbers based on their timestamps; feature analysis is performed on the data records within the time slots to identify the time characteristics present in the data rhythm, and these time characteristics are marked using phase labels; these time characteristics include local stacking, intermittent interruptions, and nonlinear time drift features. Cross-source clock offset field is used to assign timing correction values ​​to timestamps in the data source for subsequent time synchronization and data record position repair operations; The time-series anchor group is established as follows: Data records corrected by cross-source clock offset fields are mapped to spatial coordinate points determined by a unified time baseline and phase comb index number. Sliding window analysis is performed on the spatial distribution to identify high-density clusters as candidate data clusters. For each candidate data cluster, the number of records on its time axis, the distribution density of index numbers, and the consistency of offset correction values ​​are calculated to obtain a clustering strength score. Only data clusters with clustering strength scores higher than a set threshold are retained as a set of reliable anchor candidates. The data clusters in the set of reliable anchor candidates are subjected to structural stability testing. The set of data clusters that meet the preset stability conditions is defined as the time-series reliable anchor group. Among them, the consistency of offset correction values ​​refers to the consistency of the timestamp correction values ​​within the data cluster. The smaller the standard deviation, the higher the consistency. S2, under the constraint of the time-series anchor group, generates a breakpoint suspicion map and constructs a breakpoint confidence matrix through mutual information gradient and arrival time residual; Step S2 includes: Under the constraint of time-series anchor point group, the change of anchor point density is analyzed by sliding time window, the region of sudden drop in anchor point density is identified and extracted as the suspected breakpoint region, and the suspected breakpoint map is obtained based on the suspected breakpoint region. Within the time region defined by the breakpoint suspect graph, mutual information structure analysis is performed: for each pair of consecutive sliding windows, the mutual information value between the two windows is calculated, and the mutual information value reflects the degree of correlation between the windows in the time structure; the mutual information values ​​of all consecutive window pairs are arranged to form a mutual information curve, and its first derivative is calculated to obtain the mutual information gradient sequence. Based on the mutual information structure analysis, for each sliding window to be tested, all data records are extracted from it, and the time offset value of the timestamp of the data record relative to the median time of the previous window is calculated to obtain the arrival time residual of the data record in each time window. The breakpoint confidence matrix is ​​constructed by integrating anchor point density, mutual information gradient, and arrival time residual. Specifically, it includes obtaining the breakpoint confidence score for each time point based on the anchor point density, mutual information gradient, and arrival time residual, and forming the breakpoint confidence matrix with the time point as the index and the breakpoint confidence score as the value. S3, based on the constraints of the breakpoint confidence matrix, obtain the causal playback chain, replay the time segment, and calculate the gap width and drift direction; Step S3 includes: within the target time region where the confidence score identified in the breakpoint confidence matrix is ​​continuously higher than a set threshold, extracting key time segments upstream and downstream of the target time region as the behavior tracing range, and expanding forward and backward by a fixed time span with the breakpoint region as the center to form a breakpoint context sequence set; rearranging all data records in the context sequence set according to the time stamp order, and performing normalization mapping in combination with a unified time baseline to establish a time projection sequence diagram of the original data; The time projection sequence map is segmented to obtain multiple time slices of equal length; a time feature vector sequence is constructed based on the time projection sequence map, and the evolution trend of adjacent time slices in terms of density distribution, offset amplitude and time interval is analyzed to form a causal evolution path and construct a causal replay chain; dynamic replay is performed according to the node order of the causal replay chain, and a replay behavior trajectory map is generated under a unified time baseline to identify gap candidate points and evaluate gap performance. Based on the abnormal sequence segments identified from the playback behavior trajectory diagram, the gap width is calculated and the drift direction is determined; Based on the gap width, drift direction and playback behavior trajectory map, a repair prior structure is generated, and a repair condition parameter set is constructed. The repair condition parameter set is indexed by the start and end times of the breakpoint in the unified time baseline, and includes the alignment window length, the preferred order of interpolation method and the directionality suggestion of re-sampling. S4, with prior repair support, uses a multi-source aligner to align the data sources, namely the collected logs and business event times, and performs interpolation reconstruction to finally output a continuous time series. S5, based on continuous time series, utilizes a purifier to construct a critical window guardrail through manifold filtering and consistency checks; Step S5 includes: Based on the constructed continuous time series, interference window sets are identified and extracted according to the standard deviation of the recording time interval, the relative proportion of interpolation points, the consistency between the magnitude of the change in the time density gradient and the recording drift direction; Perform quantile manifold filtering on the time records within the interference window set; For each filtered interference window, the upper boundary line, lower boundary line, and behavior centerline are determined by combining the behavior offset bandwidth, time continuity score, interpolation point ratio intensity, and behavior slope stability corresponding to each interference window. Based on the upper boundary line, lower boundary line, and behavior centerline, a preliminary window guardrail is constructed. Data change consistency is verified on the preliminary window guardrail, and boundary contraction is performed based on the verification results to obtain the critical window guardrail. Here, the behavior offset bandwidth refers to the difference between the maximum magnitude of the recorded value change within the interference window and the average gradient; the data change consistency verification includes whether the data gradient is continuous and whether the numerical change conforms to unidirectional progression. S6, under the constraint of the critical window guardrail, generates the control sequence, starts the time inversion gating, drives the acquisition rhythm generator to inject supplementary acquisition instructions, performs data backfilling in the neighborhood of the breakpoint, and updates the calibration matrix back to the unified time baseline; Step S6 includes: scanning the constructed critical window guardrails, extracting the upper boundary line, lower boundary line, and behavior centerline of each critical window guardrail, and constructing a time behavior tension zone based on the upper boundary line, lower boundary line, and behavior centerline; calculating residual offset indices for time points within each time behavior tension zone, including the concentration of historical interpolation positions, the jump amplitude of data point values ​​before and after, and the cumulative offset between the current time period and the time baseline; identifying high-priority dynamic control areas based on the residual offset indices, and obtaining a target data window set based on the time interval between the high-priority dynamic control areas and the previous repair operation, and the degree of overlap between the data reconstruction behaviors before and after; based on the starting time point of each target data window in the target data window set, searching for preceding records in reverse order in the time series and gradually backtracking to construct a time backward chain; for each backtracking time point, when adjacent time points with continuous non-interpolation records and jump values ​​are identified during the backtracking process, the adjacent time points are determined as key breakpoints for triggering supplementary sampling and are used as injection points in the time inversion path; generating a set of acquisition instructions corresponding to the injection point, including the acquisition time range, data source order, and maximum number of supplementary samplings; Based on the acquisition instruction set, pulse-level data acquisition and backfilling are performed on the neighborhood of the breakpoint, insertion judgment and replacement identifier update are performed, and trajectory continuity calibration is performed; After the data backfilling operation is completed, a calibration matrix is ​​constructed and written back to a unified time baseline to complete the end-to-end correction closed loop. The construction of the calibration matrix includes: extracting multiple dimensional indicators for each re-collection point. These indicators include the maximum backtracking length of the reverse path of the re-collection point, the number of back-collection times, the interpolation coverage of the re-collected data, the continuity score of the backfilled sequence, the behavioral consistency score, and the impact on the previous repair operation. The calibration matrix is ​​constructed based on these multiple dimensional indicators.

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